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This work evaluates ten instruction-tuned models from the LLaMA-3 and Qwen-2.5 families, measuring faithfulness, minimality, and alignment with human-annotated rationales and shows that model scale is the strongest determinant of explanation quality.
AURA-Eval, a framework combining controlled augmentation with granular diagnosis of behavior in tool-use trajectories, is introduced, showing that LLM agents engage in unsafe behavior more often when no safe fulfillment path exists.
The Last Translation Benchmark is introduced, a collection of human-authored and peer-reviewed examples that break leading machine translation models and a new evaluation approach: each example comes with handcrafted verification rules describing concrete failure cases on that example, therefore allowing reliable and a...
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